Efficient Kernel Calculation of Cylindrical Antennas

This paper presents a technique for evaluating analytically and without limitation the singular part of the kernel integral of cylindrical wires due to uniform current distribution. This approach uses the static Green's function expression in cylindrical coordinates. The formula of the singular part converges rapidly and illustrates its usefulness for kernel calculations without loss of accuracy     

Efficient Calculation of Exact Group Delay Sensitivities

This paper presents, explicitly, an approach to the exact calculation of group delay and its sensitivities with respect to component parameters based on the adjoint network concept and applicable to linear time-invariant circuits. In general, no more than four analyses are required and the computational effort is only moderately more than is necessary for a single analysis. The results presented are in a form particularly suited to the computer-aided design of microwave circuits and include useful tables of sensitivity expressions.     

Computationally Efficient Predictive Robot Control

Conventional linear controllers (PID) are not really suitable for the control of robot manipulators due to the highly nonlinear behavior of the latter. Over the last decades, several control methods have been proposed to circumvent this limitation. This paper presents an approach to the control of manipulators using a computationally-efficient-model-based predictive control scheme. First, a general predictive control law is derived for position tracking and velocity control, taking into account the dynamic model of the robot, the prediction and control horizons, and also the constraints. However, the main contribution of this paper is the derivation of an analytical expression for the optimal control to be applied that does not involve a numerical procedure, as opposed to most predictive control schemes. In the last part of the paper, the effectiveness of the approach for the control of a nonlinear plant is illustrated using a direct-drive pendulum, and then, the approach is validated and compared to a PID controller using an experimental implementation on a 6-DOF cable-driven parallel manipulator.     

Computationally Efficient ESPRIT Method for Direction Finding

In this paper,a low complexity ESPRIT algorithm based on power method and Orthogo- nal-triangular (QR) decomposition is presented for direction finding,which does not require a priori knowledge of source number and the predetermined threshold (separates the signal and noise ei- gen-values).Firstly,according to the estimation of noise subspace obtained by the power method,a novel source number detection method without eigen-decomposition is proposed based on QR de- composition.Furthermore,the eigenvectors of signal subspace can be determined according to Q matrix and then the directions of signals could be computed by the ESPRIT algorithm.To determine the source number and subspace,the computation complexity of the proposed algorithm is approximated as (2log_2 n 2.67)M~3,where n is the power of covariance matrix and M is the number of array ele- ments.Compared with the Single Vector Decomposition (SVD) based algorithm,it has a substantial computational saving with the approximation performance.The simulation results demonstrate its effectiveness and robustness.     

Computationally Efficient Motion Estimation Algorithm for HEVC

In this paper, we present a novel computationally efficient motion estimation (ME) algorithm for high-efficiency video coding (HEVC). The proposed algorithm searches in the hexagonal pattern with a fixed number of search points at each grid. It utilizes the correlation between contiguous pixels within the frame. In order to reduce the computational complexity, the proposed algorithm utilizes pixel truncation, adaptive search range, sub-sampling and avoids some of the asymmetrical prediction unit techniques. Simulation results are obtained by using the reference software HM (e n c o d e r_l o w d e l a y_P_m a i n and e n c o d e r_r a n d o m a c c e s s_m a i n profile) and shows 55.49% improvement on search points with approximately the same PSNR and around 1% increment in bit rate as compared to the Test Zonal Search (TZS) ME algorithm. By utilizing the proposed algorithm, the BD-PSNR loss for the video sequences like B a s k e t b a l l P a s s_416 × 240@50 and J o h n n y_1280 × 720@60 is 0.0804 dB and 0.0392 dB respectively as compared to the HM reference software with the e n c o d e r_l o w d e l a y_P_m a i n profile.     

Computationally Efficient Nonlinearity Compensation for Coherent Fiber-Optic Systems

Split-step digital backward propagation (DBP) can be combined with coherent detection to compensate for fiber nonlinear impairments.A large number of DBP steps is usually needed for a long-haul fiber system,and this creates a heavy computational load.In a trade-off between complexity and performance,interchannel nonlinearity can be disregarded in order to simplify the DBP algorithm.The number of steps can also be reduced at the expense of performance.In periodic dispersion-managed long-haul transmission systems,optical waveform distortion is dominated by chromatic dispersion.As a result,the nonlinearity of the optical signal repeats in every dispersion period.Because of this periodic behavior,DBP of many fiber spans can be folded into one span.Using this distance-folded DBP method,the required computation for a transoceanic transmission system with full inline dispersion compensation can be reduced by up to two orders of magnitude with negligible penalty.The folded DBP method can be modified to compensate for nonlinearity in fiber links with non-zero residual dispersion per span.     

Computationally Efficient Method of Calculations Involving Lumped-Parameter Transmission-Line Models

Computations involving ladder networks may be tedious if many elements are present. Lumped-circuit iterative models are traditional approximations of transmission lines and have been used well before [1]. The number of sections used to approximate the transmission line is limited by the computational expense involved in analyzing a high-order circuit. This paper develops a closed-form expression for the voltage transfer ratio for ladder networks as a function of the number of sections. Presently, lumped-circuit models [1]-[5] are analyzed using loop-current or node-voltage equations, both time-consuming and costly. The closed-form expression developed in this paper allows one to perform rapid circuit calculations for two-conductor lines, even if the number of sections is large (e.g., 50 sections or more) with very little computational effort.     

Computationally Efficient Stochastic Realization for Internal Multiscale Autoregressive Models

In this paper we develop a stochastic realization theory for multiscale autoregressive (MAR) processes that leads to computationally efficient realization algorithms. The utility of MAR processes has been limited by the fact that the previously known general purpose realization algorithm, based on canonical correlations, leads to model inconsistencies and has complexity quartic in problem size. Our realization theory and algorithms addresses these issues by focusing on the estimation-theoretic concept of predictive efficiency and by exploiting the scale-recursive structure of so-called internal MAR processes. Our realization algorithm has complexity quadratic in problem size and with an approximation we also obtain an algorithm that has complexity linear in problem size.     

A Computationally Efficient Multilevel Coding Scheme for ISI Channels

This paper proposes a multilevel coding scheme with linear mapping for intersymbol interference (ISI) channels and derives a low-complexity receiver structure that can achieve the ISI channel capacity. The transmitter superimposes many layers of independent binary antipodal streams to generate a quadrature amplitude modulation (QAM) or Gaussian-like channel input. The receiver performs multistage decoding with decision feedback and interference cancellation. Within each stage is a linear minimum mean-square-error (MMSE) equalizer followed by an error-correcting decoder. The complexity scales linearly with the channel length and the number of layers, and the process is shown to be asymptotically information lossless if a fixed input power is properly distributed over a sufficiently large number of layers. This framework is then extended to achieve the capacity of the ISI channel using a transmitter-side spectral shaping filter that converts a Gaussian input sequence with a white spectrum to one with a water-filling spectrum.      

Computationally Efficient Single-Frequency Estimation Scheme in High-Rate WPAN Systems

Sub-optimal algorithms that avoid the complexity of the maximum likelihood scheme for estimating a frequency offset have been developed based on samples of the estimated auto-correlation function. However, their computation burden is still heavy for near-optimal performance. To overcome this problem, this paper proposes a reduced-complexity algorithm of single-frequency estimation for a high-rate wireless personal area network application. Accuracy and robustness of our frequency estimator are statistically assessed by Monte Carlo simulations. The results indicate that the proposed complexity effective algorithm closely conforms to the Cramer-Rao bound.     

Computationally Efficient Physics-Based Compact CNTFET Model for Circuit Design

We present a computationally efficient physics-based compact model designed for the conventional CNTFET featuring a MOSFET-like operation. A large part of its novelty lies on the implementation of a new analytical model of the channel charge. In addition, Boltzmann Monte Carlo (MC) simulation is performed with the challenge to cross-link this simulation technique to the compact modeling formulation. The comparison of the electrical characteristics obtained from the MC simulation and from the compact modeling demonstrates the compact model accuracy within its range of validity. Then, from a study of the CNT diameter dispersion for three technological processes, the compact model allows us to determine the CNTFET threshold voltage distribution and to evaluate the resulting dispersion of the propagation delay from the simulation of a ring oscillator.     

Computationally Efficient FIR Filtering of Polynomial Signals in DFT Domain

Fast unbiased finite impulse response (UFIR) filtering of polynomial signals can be provided in the discrete Fourier transform (DFT) domain employing fast Fourier transform (FFT). We show that the computation time can further be reduced by utilizing properties of UFIR filters in the DFT domain. The transforms have been found and investigated in detail for low-degree FIRs most widely used in practice. As a special result, we address an explicit unbiasedness condition uniquely featured to UFIR filters in DFT domain. The noise power gain and estimation error bound have also been discussed. An application is given for state estimation in a crystal clock employing the Global Positioning System based measurement of time errors provided each second. Based upon it, it is shown that filtering in the time domain takes about 1?second, which is unacceptable for real-time applications. The Kalman-like algorithm reduces the computation time by the factor of about 8, the FFT-based algorithm by about 18, and FFT with the UFIR filter DFT properties by about 20.     

Computationally Efficient Spatial Interpolators Based on Spartan Spatial Random Fields

This paper addresses the spatial interpolation of scattered data in $d$ dimensions. The problem is approached using the theory of Spartan spatial random fields (SSRFs), focusing on a specific Gaussian SSRF, i.e., the fluctuation-gradient-curvature (FGC) model. A family of spatial interpolators (predictors) is formulated by maximizing the FGC-SSRF probability density function at each prediction point, conditioned by the data. An analytical expression for the general uniform bandwidth Spartan (GUBS) predictor is derived. The linear weights of this predictor involve weighted summations of kernel functions over the sample and prediction points. Approximations for the sums are obtained at the asymptotic limit of a dense sampling network, leading to simplified explicit expressions of the weights. An asymptotic locally adaptive Spartan (ALAS) predictor is defined by means of a kernel family that involves a tunable local parameter. The relevant equations are fully developed in $d=2$. Using simulated data in two dimensions, we show that the ALAS prediction accuracy is comparable to that of ordinary kriging (OK), which is an optimal spatial linear predictor (SOLP). The numerical complexity of the ALAS predictor increases linearly with the sample size, in contrast with the cubic dependence of OK. For large data sets, the ALAS predictor is shown to be orders of magnitude faster than OK at the cost of a slightly higher prediction dispersion. The performance of the ALAS predictor and OK are compared on a data set of rainfall measurements using cross validation measures.      

A Computationally Efficient Criterion for Antenna Shuffling in DSTTD Systems

The application of double space-time transmit diversity (DSTTD) scheme to multicarrier systems, such as orthogonal frequency division multiplexing (OFDM) systems, requires calculating the determinations of the antenna permutation matrices for all subcarriers, resulting in a heavy computation load. In this paper, we show that the signal-to-noise ratio (SNR)-based antenna shuffling criterion for DSTTD systems can be reduced to a simple criterion that evaluates determinants of 2 times 2 submatrices of the 4 times 4 equivalent channel matrix. The new criterion can lighten the computational load by about 95%. Furthermore, it is shown that the minimum mean square error (MMSE)-based criterion for antenna permutation can also be reduced to the same criterion.     

Computationally Efficient Architecture for Accurate Frequency Estimation with Fourier Interpolation

A simplified DFT-based algorithm and its VLSI implementation for accurate frequency estimation of single-tone complex sinusoid signal are investigated. The proposed algorithm estimates frequency by interpolation using Fourier coefficients. It consists of a coarse search followed by a fine search, and its performance closely achieves the Cramer–Rao low bound (CRLB) even in low SNR region. Moreover, a pipelined triple-mode CORDIC architecture is designed to efficiently support complex multiplication, complex magnitude calculation and real division. The triple-mode CORDIC-based radix-4 architecture is employed for the hardware implementation of the frequency estimator, and is suitable for not only fast Fourier transformation but also accurate frequency estimation. A frequency estimator with 1024-point samples is implemented and verified on FPGA. It works at 215 MHz on a Xilinx XC6VLX240T FPGA device, and uses up 4,161 registers and 6,986 slice LUTs. ASIC synthesis results show that it requires an area of 60K equivalent NAND2 gates with a clock rate of 500 MHz at SMIC 0.18 μm technology. The whole latency of the frequency estimator is 2336 cycles. The proposed architecture provides a good trade off between hardware overhead, estimation performance and computation latency.     

A Computationally Efficient Tree-PTS Technique for PAPR Reduction of OFDM Signals

The high peak-to-average power ratio (PAPR) of time domain signals has been a major problem in orthogonal frequency division multiplexing (OFDM) systems, and thus various PAPR reduction algorithms have been introduced. Partial transmit sequence (PTS) is one of the most attractive solutions because of its good performance without distortion. However, it is considered as an impractical solution for the realization of high-speed data transmission systems due to its high computational complexity. In this paper, a novel PAPR reduction algorithm based on a tree-structured searching technique is proposed to reduce the PAPR with low complexity. In the proposed scheme, the computational complexity of searching process is decreased by adjusting the size of tree with two parameters, width and depth, while preserving good performance. The simulation results show that proposed scheme provides similar performance with optimum case with remarkably reduced computational complexity.     

Computationally Efficient Maximum Likelihood Approach to DOA Estimation of a Scattered Source

The problem of Direction-Of-Arrival (DOA)estimation in the presence of local scatterers using a uniform linear array(ULA) of sensors is addressed. We consider two models depending on whether theform of the azimuthal power distribution is explicitly known or not. For bothmodels, the block-diagonal structure of the associated Fisher InformationMatrix (FIM) is exploited to decouple the estimation of the DOA from that ofthe other model parameters. An asymptotically efficient Maximum Likelihood(ML)DOA estimator is derived which entails solving a 1-D minimization problemonly.Furthermore, the 1-D criterion can be expressed as a simple Fourier Transform.A numerical comparison with the Cramér-Rao Bound (CRB) illustrates thefactthat our computationally very simple DOA estimators are statisticallyefficientfor a wide range of scenarios.     

Design and Implementation of Computationally Efficient Image Compressor for Wireless Capsule Endoscopy

An image compressor inside wireless capsule endoscope should have low power consumption, small silicon area, high compression rate and high reconstructed image quality. Simple and efficient image compression scheme, consisting of reversible color space transformation, quantization, subsampling, differential pulse code modulation (DPCM) and Golomb–Rice encoding, is presented in this paper. To optimize these methods and combine them optimally, the unique properties of human gastrointestinal tract image are exploited. Computationally simple and suitable color spaces for efficient compression of gastrointestinal tract images are proposed. Quantization and subsampling methods are optimally combined. A hardware-efficient, locally adaptive, Golomb–Rice entropy encoder is employed. The proposed image compression scheme gives an average compression rate of 90.35 % and peak signal-to-noise ratio of 40.66 dB. ASIC has been fabricated on UMC130nm CMOS process using Faraday high-speed standard cell library. The core of the chip occupies 0.018 mm\(^2\) and consumes 35 \(\upmu {\text {W}}\) power. The experiment was performed at 2 frames per second on a \(256\times 256\) color image. The power consumption is further reduced from 35 to 9.66 \(\upmu \)W by implementing the proposed image compression scheme using Faraday low-leakage standard cell library on UMC130nm process. As compared to the existing DPCM-based implementations, our realization achieves a significantly higher compression rate for similar area and power consumption. We achieve almost as high compression rate as can be achieved with existing DCT-based image compression methods, but with an order of reduced area and power consumption.